To find strong discriminative regions for fine-grained classification tasks, we propose a plug-in module that can be applied to mainstream backbone networks such as ResNet[1]}, EfficientNet[2]}, and ViT[3]}. The overall design concept is to treat each pixel (or patch) on the feature map as an independent feature, which can represent its region. Then we classify these features and use the classification ability as a basis for distinguishing. Finally, the entire network can be completed through end-to-end training. In this chapter, the design of the module structure, the use of the loss function, and the combination with each framework will be introduced in detail.
<FIGURE>